India
USA
UK
Saudi Arabia
UAE
France
Spain
GermanyChoice of foundation models
Models from Amazon, Meta, Mistral AI, Cohere, DeepSeek, Stability AI and more behind one consistent API. When a better model arrives, you switch without rewriting your app.
Amazon Bedrock consulting
Generative AI that knows your business. Built on Amazon Bedrock.
Amazon Bedrock (Bedrock) gives you high-performing foundation models from leading AI companies through one AWS service, and your data stays private. We help you pick the right first use case, build it on your own documents and systems, and run it in production.
Amazon Bedrock
Example: an assistant on your policy PDFs
Can a customer return an opened item after 20 days?
Yes, if it is within 30 days of delivery and the item is undamaged. Opened electronics need the original box. Refunds go back to the original payment method.
Returns-Policy.pdf, section 4.2Guardrails on: personal data redacted
- 500+
- customers worldwide
- 200+
- SMEs
- 50+
- technology partners
- 24*7
- global delivery centre
- IN + UAE
- offices in India and Dubai
Talk to a Bedrock specialist
Validate your generative AI use case on your own data.
Share the business problem you want generative AI to solve. An AWS specialist will respond within one business day with a clear recommendation and next steps.
One API. A choice of foundation models.
- Nova Amazon
- Titan Amazon
- Llama Meta
- Mistral AI
- Cohere
- DeepSeek
- AI21 Labs
- Stability AI
- and more
What is available differs by AWS Region. We check what runs where your data needs to stay.
What Amazon Bedrock is
A secure AWS service for building with foundation models.
Think of Amazon Bedrock as a managed front door to foundation models. Models from Amazon, Meta, Mistral and others sit behind one API, so you are not tied to a single vendor and there are no GPU servers for your team to look after.
Around the models, Bedrock adds what a real application needs: Knowledge Bases so answers come from your documents, Guardrails to keep answers safe and on topic, tools to customise and evaluate models, and agents that can take actions in your systems.
The first question every CIO asks: what happens to our data?
Your prompts and outputs are not used to train the base models and are not shared with the model providers. Data is encrypted in transit and at rest, and traffic can stay off the public internet with VPC endpoints and AWS PrivateLink.
Where gen AI earns its keep
- Answering questions from large piles of documents
- First drafts: summaries, replies, product copy
- Pulling fields out of messy contracts, claims and forms
- Search that understands meaning, not just keywords
Where you still want people (or plain code)
- Anything that must be right every time without a check
- Exact maths and calculations, which normal software does better
- Decisions someone has to be accountable for
- Fixing bad data. Out-of-date documents give out-of-date answers
Key capabilities
Core capabilities.
Six core capabilities. Most projects use three or four, and we advise which ones fit your objectives.
Knowledge Bases (RAG)
Point Bedrock at your documents. It handles ingesting, chunking, embedding and retrieval, and every answer comes back with citations people can check.
Bedrock Guardrails
Block topics you do not want touched, filter harmful content, redact personal data, and check answers against the source with contextual grounding and Automated Reasoning checks.
Model customisation
Fine-tuning, continued pre-training and distillation on your own data, kept private. Worth it when good prompts alone do not get the tone or accuracy right.
Agents & AgentCore
Agents that do things, not just answer: look up an order, raise a ticket, update a record. Amazon Bedrock AgentCore runs them at scale.
Bedrock Data Automation
Turns documents, images, audio and video into structured data. Think invoices, claim forms and call recordings.
Use cases by industry
Where it delivers value first.
Four high-value applications of Amazon Bedrock. Most organisations start with one and scale from there.
A support assistant that answers from your policy PDFs
Customers ask about orders, returns and plans in their own words, in several languages. The assistant answers from your knowledge base and shows where the answer came from.
KYC, claims and contracts, read for you
Pull out the fields that matter from KYC documents, claims and agreements, summarise them, and hand a clean record to the person who signs off.
Contract review that used to take a week
Compare clauses against your playbook, flag what looks unusual and answer compliance questions with citations, so lawyers spend their time on the judgement calls.
Product copy at catalogue scale
Descriptions, campaign copy and translations for thousands of products, written to your brand guidelines and reviewed before they go live.
ConsultEdge Bedrock services
Our Amazon Bedrock services.
Select the engagement that matches your current stage. Each one delivers a defined, usable outcome.
3 to 4 weeks
Gen AI strategy & proof of concept
For teams with ideas but no clear first move. We find the use cases with the clearest payback and prove one on Bedrock using your data.
- Use case discovery and value scoring
- Model selection and benchmarking
- Rapid PoC on Amazon Bedrock
- Responsible AI and risk review
You walk away with
A working PoC, evaluation results and a roadmap to production.
4 to 8 weeks
RAG & knowledge assistants
Assistants and search that answer from your documents, policies and data with citations, built on Bedrock Knowledge Bases and Amazon OpenSearch.
- Ingestion from S3, SharePoint, Confluence and more
- Chunking, embedding and retrieval tuning
- Access-aware retrieval, so people only see what they are allowed to
- Front ends in the web or your team chat tools
You walk away with
A production assistant with measured answer accuracy.
4 to 10 weeks
Fine-tuning & model customisation
When good prompts are not enough. We fine-tune or distil a model for your domain to improve accuracy, tone or cost.
- Training data preparation and labelling
- Fine-tuning and distillation on Bedrock
- Evaluation against baseline models
- Amazon SageMaker when needs go further
You walk away with
A customised model with documented gains in quality and cost.
2 to 4 weeks
Security, Guardrails & compliance
Our CERT-In empanelled security practice makes your gen AI apps safe, private and ready for audit.
- Bedrock Guardrails configuration
- Private networking with VPC and PrivateLink
- Prompt injection and LLM security testing
- Governance aligned to ISO 27001 and India's DPDP Act
You walk away with
Security architecture, test reports and compliance evidence.
Ongoing
Managed GenAIOps, 24*7
Going live is the start, not the finish. Our Global Delivery Centre keeps your Bedrock apps accurate, reliable and within budget.
- Quality and drift monitoring
- Token usage and cost tuning
- Model upgrades with regression tests
- Incident management against SLAs
You walk away with
A monthly report on quality, usage and cost, and a team on call.
FAQs
Frequently asked questions.
The questions business and tech leads ask us most about Amazon Bedrock.
Submit your questionAmazon Bedrock is a fully managed AWS service that gives you high-performing foundation models from leading AI companies through one API, plus the tools to build real applications with them: Knowledge Bases for answering from your data, Guardrails, agents, model customisation and evaluation. There are no servers or GPUs for you to manage.
Models from Amazon (Nova and Titan), Meta (Llama), Mistral AI, Cohere, DeepSeek, AI21 Labs and Stability AI, among others. What is available differs by AWS Region, so we confirm the list for the Region your data needs to stay in.
No. Your prompts and outputs are not used to train the base models and are not shared with the model providers. Data is encrypted in transit and at rest, and you can keep traffic off the public internet with VPC endpoints and AWS PrivateLink.
Retrieval-augmented generation (RAG) means the model looks up relevant passages in your own content before it answers, instead of relying only on what it learned in training. Bedrock Knowledge Bases run the whole pipeline (ingesting, embedding, storing and retrieving) and return citations so people can check the answer.
